Fault Diagnosis of Feedwater Pump in Nuclear Power Plants Using Parameter-Optimized Support Vector Machine

Yu, Rui; Li, Xianling; Tao, Mu‐Xuan; Ke, Zhiwu

doi:10.1115/icone24-60334

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Sixty-seven sets of signals were generated, of which forty sets were used to train the model and twenty-seven sets were used to test the accuracy of the model. Feature extraction was performed for seven different signals, and classification was performed using six algorithms-ELM [26], SVM [27], KNN [28], NB [29], NN [30], and DTA [31]-and the recognition rate of the classification results is shown in Figure 8, from which we can see that the accuracy of classification recognition using the ELM algorithm was 92.86%, which was able to identify 100% of bias, blocking, drift, period, and internal fault signals, respectively. The accuracy of classification recognition using the SVM algorithm was the same as that of ELM algorithm; the accuracy of classification recognition using the KNN algorithm was 89.29%, whereby it was able to identify 100% of bias, blocking, drift, period and internal fault signals, 50% of normal signals and 75% of multiplicative signals.…”

Section: Simulationmentioning

confidence: 99%

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

Cui

Zhang

et al. 2023

Micromachines

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MEMS gyroscopes are one of the core components of inertial navigation systems. The maintenance of high reliability is critical for ensuring the stable operation of the gyroscope. Considering the production cost of gyroscopes and the inconvenience of obtaining a fault dataset, in this study, a self-feedback development framework is proposed, in which a dualmass MEMS gyroscope fault diagnosis platform is designed based on MATLAB/Simulink simulation, data feature extraction, and classification prediction algorithm and real data feedback verification. The platform integrates the dualmass MEMS gyroscope Simulink structure model and the measurement and control system, and reserves various algorithm interfaces for users to independently program, which can effectively identify and classify seven kinds of signals of the gyroscope: normal, bias, blocking, drift, multiplicity, cycle and internal fault. After feature extraction, six algorithms, ELM, SVM, KNN, NB, NN, and DTA, were respectively used for classification prediction. The ELM and SVM algorithms had the best effect, and the accuracy of the test set was up to 92.86%. Finally, the ELM algorithm is used to verify the actual drift fault dataset, and all of them are successfully identified.

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Section: Simulationmentioning

confidence: 99%

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

Cui

Zhang

et al. 2023

Micromachines

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“…bearing failures, cavitation, unbalance, misalignment, etc. The literature contains many examples of proposals for RUL prediction of components applied to centrifugal pumps, some of which can be found in [7][8][9][10][11][12][13][14][15][16][17][18]. On the other hand, Li X. et al in [19] analyse the prognosis techniques of rotating equipment using system-level models, the purpose in this case is the prediction of the RUL at a system level.…”

Section: Fig 1 Diagnosis Y Prognosis Approaches [3]mentioning

confidence: 99%

Work-in-progress: Reliability prediction of API centrifugal pumps using survival analysis

Vila Forteza,

Galar,

Kumar

et al. 2023

Proceedings of the 19th IMEKO TC10 International Conference on Measurement for Diagnostics, Optimization and Control on Measure

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In the Oil & Gas Industry, large fleets of centrifugal pumps are used for different services working in diverse process conditions. More specifically in oil refineries, they have many characteristics in common since they are centrifugal machines that handle liquids using the same operating principle and because their design is highly standardized by API 610/ISO 13709 std for centrifugal pumps and API 682 std for sealing systems. As well, operating units and refining processes are well known and do not differ much regardless of where they are installed. Due to their criticality in the refining process, the reliability of these assets is of the utmost importance, being the MTBF (Mean Time Between Failures) one of the most used KPI (Key Performance Indicator) for evaluating it. Considering the characteristics indicated above, the possibility of predicting the MTBF of centrifugal pumps based on historical failure data, design features and expected operating conditions with Cox Proportional Hazards Model (PHM) is suggested.

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“…The SVM technique has been used for classification data sets in recent years and has been proven to have achieved satisfactory results in various studies. [45][46][47] First, the SVM algorithm was invented by Vapnik and Learner in 1963 for pattern recognition. 48 Subsequently, the widespread use of this classification technique by ML engineers increased with the increase in the speed of computer technologies since the 2000s.…”

Section: Machine Learning Techniques For Aircraft Hydraulic System Fa...mentioning

confidence: 99%

A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques

Kosova

Ünver

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Since the last decade, aircraft systems, such as flight control and landing gear, have been requiring increasing power, and consequently, the complexity of hydraulic aircraft systems has escalated. Inevitably, this complexity has resulted in the need for the troubleshooting of hydraulic aircraft systems that are dispersed around an aircraft and supply power to critical flight systems. This study proposes a novel digital twin-based health monitoring system for aircraft hydraulic systems to enable diagnostics of system failures early in the design cycle using machine learning (ML) methods. The scope of the systems is limited to hydraulic systems at the aircraft level using 20 failure scenarios. The support vector machine and several ensemble learning algorithms of ML methods were used to identify these failures. A comparison of the ML methods revealed that the random forest algorithm performed superior to the other ML algorithms. The developed digital twin framework for hydraulic system of aerial vehicle platforms, can help researchers and engineers to evaluate diagnostics systems early in the design phase.

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Fault Diagnosis of Feedwater Pump in Nuclear Power Plants Using Parameter-Optimized Support Vector Machine

Cited by 6 publications

References 7 publications

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

Work-in-progress: Reliability prediction of API centrifugal pumps using survival analysis

A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques

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